Papers with reconstruction quality
AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders (2026.eacl-long)
Copied to clipboard
Georgii Aparin, Tasnima Sadekova, Alexey Rukhovich, Assel Yermekova, Laida Kushnareva, Vadim Popov, Kristian Kuznetsov, Irina Piontkovskaya
| Challenge: | Feature steering reduces Whisper’s false speech detections by 70% with negligible WER increase, demonstrating real-world applicability. |
| Approach: | They train Sparse Autoencoders across all encoder layers of Whisper and HuBERT and evaluate their stability, interpretability, and practical utility. |
| Outcome: | The proposed models capture general acoustic and semantic information as well as specific events, including environmental noises and paralinguistic sounds, and disentangle them effectively. |
PyramidCodec: Hierarchical Codec for Long-form Music Generation in Audio Domain (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to generate long music are inefficient and lack of structured representation. |
| Approach: | They propose a hierarchical discrete representation of audio for long audio-domain music generation using residual vector quantization on different levels of features. |
| Outcome: | The proposed method achieves competitive performance in terms of reconstruction quality and token per second (TPS) the proposed method facilitates training a language model that can generate well-structured long-form music for up to 3 minutes. |
Group-SAE: Efficient Training of Sparse Autoencoders for Large Language Models via Layer Groups (2025.emnlp-main)
Copied to clipboard
| Challenge: | Sparse Autoencoders (SAEs) are a promising unsupervised approach for understanding the representations of layers of Large Language Models (LLMs). |
| Approach: | They propose a method that groups similar models and trains a single SAE per group based on representational similarity across layers. |
| Outcome: | Experiments on Pythia family models show that the proposed method significantly accelerates training with minimal impact on reconstruction quality and comparable downstream task performance and interpretability over baseline SAEs trained layer by layer. |